Using If else to check multiple columns and create a new column based on the response for string responses

Question

I have the following dataset:

hairdf=data.frame(
id=c(1:4),
  typedad=c("straight*","curly"),
  colourdad=c("brown","black"),
  typemom=c("curly","wavy*"),
  colourmom=c("blonde","red"),
  typekid1=c("wavy","mixed*"),
  colourkid1=c("black","blonde"))

I want to create new columns that will look at hairtypes and give value 1 if the type of hair appears in "hairtype" columns without an asterisk and a value 2 if it appears with an asterisk (blank if it doesnt appear in that row). It should look like so:

id	typedad	colourdad	typemom	colourmom	typekid1	colourkid1	straight	curly	wavy	mixed
1	striaght*	brown	curly	blonde	wavy	black	2	1	1
2	curly	black	wavy*	red	mixed*	blonde		1	2	2

My two issues are that all other examples use numeric values and all other examples have the columns of interest located next to each other. I need code that looks to match strings in columns that can be located anywhere in the dataframe. I have tried the following:

straight<- hairdf %>% mutate(across(c("hairtypedad", "hairtypemom", "hairtypekid1"),
                                    ifelse(.=="straight", 1
                                             ifelse(.=="straight*",2, ""
                                             ))))
curly<- hairdf %>% mutate(across(c("hairtypedad", "hairtypemom", "hairtypekid1"),
                                        ifelse(.=="curly", 1
                                                 ifelse(.=="curly*",2, ""
 wavy<- hairdf %>% mutate(across(c("hairtypedad", "hairtypemom", "hairtypekid1"),
                                        ifelse(.=="wavy", 1
                                                 ifelse(.=="wavy*",2, ""
                                                 ))))      
mixed<- hairdf %>% mutate(across(c("hairtypedad", "hairtypemom", "hairtypekid1"),
                                        ifelse(.=="mixed", 1
                                                 ifelse(.=="mixed*",2, ""
                                                 )))) 

But I'm not sure if this code even makes sense. Also, this will be tedious as I have way more hairtypes, so any suggestions to make it easier would be appreciated as well!! Thankyou!!!

Answer 1

This is not the more efficient answer, neither the more general solution, but may satisfy a solution:

#create columns
st <- rep(NA,nrow(hairdf));
cur <- rep(NA,nrow(hairdf));
wav <- rep(NA,nrow(hairdf));
mix <- rep(NA,nrow(hairdf));

#join and define words
hairdf <- cbind(hairdf,st,cur,wav,mix);
words <- c("straight","curly","wavy","mixed");
words_ast <- paste(words,"*",sep=""); #just get the "*" words

#make a loop according to positions of columns st,cur,wav,mix
for (j in 1:length(words_ast)){ #let's see if we can evaluate 2 in words_ast
  for (i in c(2,3,4)){ #but only in columns we selected
    a <- subset(hairdf,hairdf[,i]==words_ast[j]) #subset columns which satisfay condition. [Note that this can be written as hairdf %>% subset(.[,i]==words_ast[j]) ]
    hairdf[row.names(a),7+j] <- 2 #replace value from column 8
  }
}
#repeat process for "words"

for (j in 1:length(words)){
  for (i in c(2,3,4)){
    a <- subset(hairdf,hairdf[,i]==words[j])
    hairdf[row.names(a),7+j] <- 1
  }
}

This should allow you to get the expected result. Alternatively, you can use the assign() function, i.e

assign(x,value=1)

where x is each element in words.

So in a loop:

assign(words[n],value=1) ; assign(words_ast[n],value=2)